The subject of this thesis is policing at a local level with regard to how performance can be measured and compared between locations. The objective is to help make the police more accountable to the public and to increase the public’s confidence in the police. The research is carried out within the discipline of Geographic Information Science using multidimensional, multivariate clustering and classification techniques to create geo-policing classifications in ways similar to those used to create geodemographic classifications. The research explores the causes of the mismatches between public perceptions of the level of crime and the information available through official crime statistics that has been termed the ‘Reassurance Gap’. Reassurance policing has led to a significant shift of resources towards council ward based neighbourhood policing, in order to alleviate the fear of crime and increase public confidence in the police. Subsequent research has shown that reductions in the fear of crime are not directly linked to levels of public confidence in the police. The research suggests that the most effective method through which the police might garner increased public confidence is by demonstrating that they properly understand policing problems in neighbourhoods and that they are efficiently, effectively and fairly tackling those problems. The research examines the use of crime maps designed for the public to improve police accountability and public confidence in the police. The research concludes that official crime statistics are not suitable for assessing local police accountability and makes the case for using police incident data instead. The thesis shows the utility of police incident data and creates a framework that allows policing itself rather than just the outcomes of policing to be assessed.